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Stogiannos N, Gillan C, Precht H, Reis CSD, Kumar A, O'Regan T, Ellis V, Barnes A, Meades R, Pogose M, Greggio J, Scurr E, Kumar S, King G, Rosewarne D, Jones C, van Leeuwen KG, Hyde E, Beardmore C, Alliende JG, El-Farra S, Papathanasiou S, Beger J, Nash J, van Ooijen P, Zelenyanszki C, Koch B, Langmack KA, Tucker R, Goh V, Turmezei T, Lip G, Reyes-Aldasoro CC, Alonso E, Dean G, Hirani SP, Torre S, Akudjedu TN, Ohene-Botwe B, Khine R, O'Sullivan C, Kyratsis Y, McEntee M, Wheatstone P, Thackray Y, Cairns J, Jerome D, Scarsbrook A, Malamateniou C. A multidisciplinary team and multiagency approach for AI implementation: A commentary for medical imaging and radiotherapy key stakeholders. J Med Imaging Radiat Sci 2024; 55:101717. [PMID: 39067309 DOI: 10.1016/j.jmir.2024.101717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2024] [Accepted: 06/27/2024] [Indexed: 07/30/2024]
Affiliation(s)
- Nikolaos Stogiannos
- Division of Midwifery & Radiography, City, University of London, United Kingdom; Magnitiki Tomografia Kerkiras, Corfu, Greece.
| | - Caitlin Gillan
- Joint Department of Medical Imaging, University Health Network, Canada; Departments of Radiation Oncology & Medical Imaging, University of Toronto, Toronto, Canada
| | - Helle Precht
- Health Sciences Research Centre, UCL University College, Radiology Department, Lillebelt Hospital, University Hospitals of Southern Denmark, Institute of Regional Health Research, University of Southern Denmark, Discipline of Medical Imaging and Radiation Therapy, University College Cork, Ireland
| | - Claudia Sa Dos Reis
- School of Health Sciences (HESAV), University of Applied Sciences and Arts Western Switzerland (HES-SO), Lausanne, Switzerland
| | - Amrita Kumar
- Frimley Health NHS Foundation Trust, British Institute of Radiology, United Kingdom
| | - Tracy O'Regan
- The Society and College of Radiographers, London, United Kingdom
| | | | - Anna Barnes
- King's Technology Evaluation Centre, School of biomedical engineering and imaging sciences, King's College London, United Kingdom
| | - Richard Meades
- Department of Nuclear Medicine, Royal Free London NHS Foundation, London, United Kingdom
| | | | - Julien Greggio
- Division of Midwifery & Radiography, City, University of London, United Kingdom; Italian Association of MR Radiographers, Cagliari, Italy
| | - Erica Scurr
- Department of Radiology, Royal Marsden Hospital, London, United Kingdom
| | | | - Graham King
- Annalise.ai Pty Ltd, Sydney, Australia; AI Special Focus Group, AXREM Association of Healthcare Technology Providers for Imaging Radiotherapy and Care, London, United Kingdom
| | | | - Catherine Jones
- Royal Brisbane and Womens' Hospital, Brisbane, Australia; I-MED Radiology, Brisbane, Australia; Faculty of Medicine and Health, University of Sydney, Sydney, Australia
| | - Kicky G van Leeuwen
- Romion Health, Utrecht, the Netherlands; Health AI Register, Utrecht, the Netherlands
| | - Emma Hyde
- University of Derby, Derby, United Kingdom
| | | | | | - Samar El-Farra
- Emirates Medical Society - The Radiographers Society of Emirates (RASE), United Arab Emirates
| | | | - Jan Beger
- Science and Technology Organisation, GE HealthCare, United States
| | - Jonathan Nash
- University Hospitals Sussex, United Kingdom; Kheiron Medical Technologies, London, United Kingdom; British Society of Breast Radiology, the Netherlands
| | - Peter van Ooijen
- Dept of Radiotherapy and Data Science Center in Health (DASH), University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Christiane Zelenyanszki
- Community Diagnostics, Barking, Havering and Redbridge University Hospitals NHS Trust, United Kingdom
| | - Barbara Koch
- Jheronimus Academy of Data Science, the Netherlands; Tilburg University, the Netherlands
| | | | | | - Vicky Goh
- School of Biomedical Engineering and Imaging Sciences, King's College London. Department of Radiology, Guy's & St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Tom Turmezei
- Norwich Medical School, University of East Anglia, United Kingdom; Department of Radiology, Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich, United Kingdom
| | | | | | - Eduardo Alonso
- Artificial Intelligence Research Centre, City, University of London, United Kingdom
| | - Geraldine Dean
- ESTH NHS Trust, United Kingdom; NHS SW London Imaging Network, United Kingdom
| | - Shashivadan P Hirani
- Centre for Healthcare Innovation Research, City, University of London, London, United Kingdom
| | - Sofia Torre
- Frimley Health Foundation Trust, United Kingdom
| | - Theophilus N Akudjedu
- Institute of Medical Imaging & Visualisation, Department of Medical Science & Public Health, Faculty of Health & Social Sciences, Bournemouth University, United Kingdom
| | - Benard Ohene-Botwe
- Department of Midwifery & Radiography, City, University of London, United Kingdom
| | - Ricardo Khine
- Institute of Health Sciences Education, Faculty of Medicine and Dentistry, Queen Mary, University of London, United Kingdom
| | - Chris O'Sullivan
- Department of Midwifery & Radiography, School of Health & Psychological Sciences, City, University of London, United Kingdom
| | - Yiannis Kyratsis
- Erasmus School of Health Policy and Management, Erasmus University Rotterdam, the Netherlands
| | - Mark McEntee
- Discipline of Medical Imaging and Radiation Therapy, University College Cork, Ireland; Institute of Regional Health Research, University of Southern Denmark, Denmark; Faculty of Health Sciences, The University of Sydney, Australia
| | | | | | - James Cairns
- Department of Radiology, Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom; Faculty of Medicine & Health, University of Leeds, Leeds, United Kingdom
| | | | - Andrew Scarsbrook
- Department of Radiology, Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom; Faculty of Medicine & Health, University of Leeds, Leeds, United Kingdom
| | - Christina Malamateniou
- Department of Midwifery & Radiography, City, University of London, United Kingdom; European Society of Medical Imaging Informatics, Vienna, Austria
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Finnegan RN, Quinn A, Booth J, Belous G, Hardcastle N, Stewart M, Griffiths B, Carroll S, Thwaites DI. Cardiac substructure delineation in radiation therapy - A state-of-the-art review. J Med Imaging Radiat Oncol 2024. [PMID: 38757728 DOI: 10.1111/1754-9485.13668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Accepted: 04/29/2024] [Indexed: 05/18/2024]
Abstract
Delineation of cardiac substructures is crucial for a better understanding of radiation-related cardiotoxicities and to facilitate accurate and precise cardiac dose calculation for developing and applying risk models. This review examines recent advancements in cardiac substructure delineation in the radiation therapy (RT) context, aiming to provide a comprehensive overview of the current level of knowledge, challenges and future directions in this evolving field. Imaging used for RT planning presents challenges in reliably visualising cardiac anatomy. Although cardiac atlases and contouring guidelines aid in standardisation and reduction of variability, significant uncertainties remain in defining cardiac anatomy. Coupled with the inherent complexity of the heart, this necessitates auto-contouring for consistent large-scale data analysis and improved efficiency in prospective applications. Auto-contouring models, developed primarily for breast and lung cancer RT, have demonstrated performance comparable to manual contouring, marking a significant milestone in the evolution of cardiac delineation practices. Nevertheless, several key concerns require further investigation. There is an unmet need for expanding cardiac auto-contouring models to encompass a broader range of cancer sites. A shift in focus is needed from ensuring accuracy to enhancing the robustness and accessibility of auto-contouring models. Addressing these challenges is paramount for the integration of cardiac substructure delineation and associated risk models into routine clinical practice, thereby improving the safety of RT for future cancer patients.
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Affiliation(s)
- Robert N Finnegan
- Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, New South Wales, Australia
- Institute of Medical Physics, School of Physics, University of Sydney, Sydney, New South Wales, Australia
| | - Alexandra Quinn
- Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, New South Wales, Australia
| | - Jeremy Booth
- Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, New South Wales, Australia
- Institute of Medical Physics, School of Physics, University of Sydney, Sydney, New South Wales, Australia
| | - Gregg Belous
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Brisbane, Queensland, Australia
| | - Nicholas Hardcastle
- Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia
| | - Maegan Stewart
- Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, New South Wales, Australia
- School of Health Sciences, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia
| | - Brooke Griffiths
- Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, New South Wales, Australia
| | - Susan Carroll
- Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, New South Wales, Australia
- School of Health Sciences, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia
| | - David I Thwaites
- Institute of Medical Physics, School of Physics, University of Sydney, Sydney, New South Wales, Australia
- Radiotherapy Research Group, Leeds Institute of Medical Research, St James's Hospital and University of Leeds, Leeds, UK
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Manson EN, Hasford F, Trauernicht C, Ige TA, Inkoom S, Inyang S, Samba O, Khelassi-Toutaoui N, Lazarus G, Sosu EK, Pokoo-Aikins M, Stoeva M. Africa's readiness for artificial intelligence in clinical radiotherapy delivery: Medical physicists to lead the way. Phys Med 2023; 113:102653. [PMID: 37586146 DOI: 10.1016/j.ejmp.2023.102653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 07/30/2023] [Accepted: 08/05/2023] [Indexed: 08/18/2023] Open
Abstract
BACKGROUND There have been several proposals by researchers for the introduction of Artificial Intelligence (AI) technology due to its promising role in radiotherapy practice. However, prior to the introduction of the technology, there are certain general recommendations that must be achieved. Also, the current challenges of AI must be addressed. In this review, we assess how Africa is prepared for the integration of AI technology into radiotherapy service delivery. METHODS To assess the readiness of Africa for integration of AI in radiotherapy services delivery, a narrative review of the available literature from PubMed, Science Direct, Google Scholar, and Scopus was conducted in the English language using search terms such as Artificial Intelligence, Radiotherapy in Africa, Machine Learning, Deep Learning, and Quality Assurance. RESULTS We identified a number of issues that could limit the successful integration of AI technology into radiotherapy practice. The major issues include insufficient data for training and validation of AI models, lack of educational curriculum for AI radiotherapy-related courses, no/limited AI teaching professionals, funding, and lack of AI technology and resources. Solutions identified to facilitate smooth implementation of the technology into radiotherapy practices within the region include: creating an accessible national data bank, integrating AI radiotherapy training programs into Africa's educational curriculum, investing in AI technology and resources such as electronic health records and cloud storage, and creation of legal laws and policies to support the use of the technology. These identified solutions need to be implemented on the background of creating awareness among health workers within the radiotherapy space. CONCLUSION The challenges identified in this review are common among all the geographical regions in the African continent. Therefore, all institutions offering radiotherapy education and training programs, management of the medical centers for radiotherapy and oncology, national and regional professional bodies for medical physics, ministries of health, governments, and relevant stakeholders must take keen interest and work together to achieve this goal.
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Affiliation(s)
| | | | | | | | | | | | - Odette Samba
- General Hospital of Yaoundé and University of Yaoundé I, Cameroon.
| | | | - Graeme Lazarus
- Inkosi Albert Luthuli Central Hospital, Durban, South Africa.
| | - Edem Kwabla Sosu
- School of Nuclear and Allied Sciences, University of Ghana, Ghana.
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